scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization DOI Creative Commons
Bowen Zhao,

Kailu Song,

Dong‐Qing Wei

и другие.

Communications Biology, Год журнала: 2025, Номер 8(1)

Опубликована: Фев. 13, 2025

Abstract The rapid advancement of single-cell technologies has created an urgent need for effective methods to integrate and harmonize data. Technical biological variations across studies complicate data integration, while conventional tools often struggle with reliance on gene expression distribution assumptions over-correction. Here, we present scCobra, a deep generative neural network designed overcome these challenges through contrastive learning domain adaptation. scCobra effectively mitigates batch effects, minimizes over-correction, ensures biologically meaningful integration without assuming specific distributions. It enables online label transfer datasets allowing continuous new retraining. Additionally, supports effect simulation, advanced multi-omic scalable processing large datasets. By integrating harmonizing from similar studies, expands the available investigating problems, improving cross-study comparability, revealing insights that may be obscured in isolated

Язык: Английский

Time-resolved single-cell RNA-seq using metabolic RNA labelling DOI
Florian Erhard, Antoine‐Emmanuel Saliba, Alexandra Lusser

и другие.

Nature Reviews Methods Primers, Год журнала: 2022, Номер 2(1)

Опубликована: Сен. 29, 2022

Язык: Английский

Процитировано

43
Making single-cell proteomics biologically relevant DOI
Florian A. Rosenberger, Marvin Thielert, Matthias Mann

и другие.

Nature Methods, Год журнала: 2023, Номер 20(3), С. 320 - 323

Опубликована: Март 1, 2023

Язык: Английский

Процитировано

38
Defining Interactions Between the Genome, Epigenome, and the Environment in Inflammatory Bowel Disease: Progress and Prospects DOI Creative Commons
Alexandra Noble, Jan Krzysztof Nowak, Alex Adams

и другие.

Gastroenterology, Год журнала: 2023, Номер 165(1), С. 44 - 60.e2

Опубликована: Апрель 14, 2023

Язык: Английский

Процитировано

38
High sensitivity top–down proteomics captures single muscle cell heterogeneity in large proteoforms DOI Creative Commons
Jake A. Melby, Kyle A. Brown, Zachery R. Gregorich

и другие.

Proceedings of the National Academy of Sciences, Год журнала: 2023, Номер 120(19)

Опубликована: Май 1, 2023

Single-cell proteomics has emerged as a powerful method to characterize cellular phenotypic heterogeneity and the cell-specific functional networks underlying biological processes. However, significant challenges remain in single-cell for analysis of proteoforms arising from genetic mutations, alternative splicing, post-translational modifications. Herein, we have developed highly sensitive functionally integrated top–down comprehensive single cells. We applied this muscle fibers (SMFs) resolve their heterogeneous proteomic properties at level. Notably, detected large (>200 kDa) SMFs. Using SMFs obtained three distinct muscles, found fiber-to-fiber among sarcomeric which can be related heterogeneity. Importantly, multiple isoforms myosin heavy chain (~223 kDa), motor protein that drives contraction, with high reproducibility enable classification individual fiber types. This study reveals cell establishes direct relationship between types, highlighting potential uncovering molecular underpinnings cell-to-cell variation complex systems.

Язык: Английский

Процитировано

36
The heart field transcriptional landscape at single-cell resolution DOI Creative Commons
Robert G. Kelly

Developmental Cell, Год журнала: 2023, Номер 58(4), С. 257 - 266

Опубликована: Фев. 1, 2023

Язык: Английский

Процитировано

26
Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps DOI Creative Commons
Hannah Spitzer, Scott M. Berry, Mark W. Donoghoe

и другие.

Nature Methods, Год журнала: 2023, Номер 20(7), С. 1058 - 1069

Опубликована: Май 29, 2023

Abstract Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder Multiplexed Pixel Analysis), which uses conditional variational autoencoder to learn representations molecular pixel profiles that are consistent across heterogeneous cell populations experimental perturbations. Clustering these pixel-level identifies subcellular landmarks, can be quantitatively compared in terms their size, shape, composition relative organization. Using high-resolution immunofluorescence, this reveals organization changes upon perturbation RNA synthesis, processing or uncovers links between membraneless organelles cell-to-cell variability bulk synthesis rates. By capturing interpretable cellular phenotypes, anticipate will greatly accelerate systematic mapping multiscale atlases biological identify rules by physiology disease.

Язык: Английский

Процитировано

22
Aging atlas reveals cell-type-specific effects of pro-longevity strategies DOI Creative Commons
Shihong Max Gao, Yanyan Qi, Qinghao Zhang

и другие.

Nature Aging, Год журнала: 2024, Номер 4(7), С. 998 - 1013

Опубликована: Май 30, 2024

Abstract Organismal aging involves functional declines in both somatic and reproductive tissues. Multiple strategies have been discovered to extend lifespan across species. However, how age-related molecular changes differ among various tissues those lifespan-extending slow tissue distinct manners remain unclear. Here we generated the transcriptomic Cell Atlas of Worm Aging (CAWA, http://mengwanglab.org/atlas ) wild-type long-lived strains. We cell-specific, signatures all germ cell types. developed clocks for different quantitatively determined three pro-longevity distinctively. Furthermore, through genome-wide profiling alternative polyadenylation (APA) events tissues, cell-type-specific APA during revealed these are differentially affected by strategies. Together, this study offers fundamental insights into provides a valuable resource in-depth understanding diversity mechanisms.

Язык: Английский

Процитировано

14
scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders DOI Creative Commons

Yichuan Cao,

Xiamiao Zhao,

Songming Tang

и другие.

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Апрель 6, 2024

Abstract Recent advancements for simultaneously profiling multi-omics modalities within individual cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy. However, technical limitations lead to highly noisy multi-modal data substantial costs. Although computational methods been proposed translate single-cell across modalities, broad applications still remain impeded by formidable challenges. Here, we propose scButterfly, a versatile cross-modality translation method based on dual-aligned variational autoencoders augmentation schemes. With comprehensive experiments multiple datasets, provide compelling evidence scButterfly’s superiority over baseline in preserving while translating datasets various contexts revealing cell type-specific biological insights. Besides, demonstrate extensive scButterfly integrative analysis single-modality data, enhancement poor-quality multi-omics, automatic type annotation scATAC-seq data. Moreover, can be generalized unpaired training, perturbation-response analysis, consecutive translation.

Язык: Английский

Процитировано

11
Single Cell Atlas: a single-cell multi-omics human cell encyclopedia DOI Creative Commons
Lu Pan, Paolo Parini, Roman Tremmel

и другие.

Genome biology, Год журнала: 2024, Номер 25(1)

Опубликована: Апрель 19, 2024

Abstract Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations from five omics, spatial transcriptomics, two bulk omics across 125 healthy adult fetal tissues. We its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration deep signatures The atlas resources database queries aspire serve as one-stop, comprehensive, time-effective resource various studies.

Язык: Английский

Процитировано

11
Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks DOI Creative Commons
Xin Sui,

Jennifer A. Lo,

Shuchen Luo

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Авг. 7, 2024

Abstract Characterizing the transcriptional and translational gene expression patterns at single-cell level within their three-dimensional (3D) tissue context is essential for revealing how genes shape structure function in health disease. However, most existing spatial profiling techniques are limited to 5-20 µm thin sections. Here, we developed Deep-STARmap Deep-RIBOmap, which enable 3D situ quantification of thousands transcripts corresponding translation activities, respectively, 200-µm thick blocks. This achieved through scalable probe synthesis, hydrogel embedding with efficient anchoring, robust cDNA crosslinking. We first utilized combination multicolor fluorescent protein imaging simultaneous molecular cell typing neuron morphology tracing mouse brain. also demonstrate that facilitates comprehensive quantitative analysis tumor-immune interactions human skin cancer.

Язык: Английский

Процитировано

10